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2021
03-05

python multiprocessing 多进程并行计算的操作

python的multiprocessing包是标准库提供的多进程并行计算包,提供了和threading(多线程)相似的API函数,但是相比于threading,将任务分配到不同的CPU,避免了GIL(Global Interpreter Lock)的限制。

下面我们对multiprocessing中的Pool和Process类做介绍。

Pool

采用Pool进程池对任务并行处理更加方便,我们可以指定并行的CPU个数,然后 Pool 会自动把任务放到进程池中运行。 Pool 包含了多个并行函数。

apply apply_async

apply 要逐个执行任务,在python3中已经被弃用,而apply_async是apply的异步执行版本。并行计算一定要采用apply_async函数。

import multiprocessing
import time
from random import randint, seed
def f(num):
  seed()
  rand_num = randint(0,10) # 每次都随机生成一个停顿时间
  time.sleep(rand_num)
  return (num, rand_num)
start_time = time.time()
cores = multiprocessing.cpu_count()
pool = multiprocessing.Pool(processes=cores)
pool_list = []
result_list = []
start_time = time.time()
for xx in xrange(10):
  pool_list.append(pool.apply_async(f, (xx, ))) # 这里不能 get, 会阻塞进程
result_list = [xx.get() for xx in pool_list]
#在这里不免有人要疑问,为什么不直接在 for 循环中直接 result.get()呢?这是因为pool.apply_async之后的语句都是阻塞执行的,调用 result.get() 会等待上一个任务执行完之后才会分配下一个任务。事实上,获取返回值的过程最好放在进程池回收之后进行,避免阻塞后面的语句。
# 最后我们使用一下语句回收进程池:  
pool.close()
pool.join()
print result_list
print '并行花费时间 %.2f' % (time.time() - start_time)
print '串行花费时间 %.2f' % (sum([xx[1] for xx in result_list]))
#[(0, 8), (1, 2), (2, 4), (3, 9), (4, 0), (5, 1), (6, 8), (7, 3), (8, 4), (9, 6)]
#并行花费时间 14.11
#串行花费时间 45.00

map map_async

map_async 是 map的异步执行函数。

相比于 apply_async, map_async 只能接受一个参数。

import time
from multiprocessing import Pool
def run(fn):
 #fn: 函数参数是数据列表的一个元素
 time.sleep(1)
 return fn*fn
if __name__ == "__main__":
 testFL = [1,2,3,4,5,6] 
 print '串行:' #顺序执行(也就是串行执行,单进程)
 s = time.time()
 for fn in testFL:
  run(fn)
 e1 = time.time()
 print "顺序执行时间:", int(e1 - s)
 print '并行:' #创建多个进程,并行执行
 pool = Pool(4) #创建拥有5个进程数量的进程池
 #testFL:要处理的数据列表,run:处理testFL列表中数据的函数
 rl =pool.map(run, testFL) 
 pool.close()#关闭进程池,不再接受新的进程
 pool.join()#主进程阻塞等待子进程的退出
 e2 = time.time()
 print "并行执行时间:", int(e2-e1)
 print rl
# 串行:
# 顺序执行时间: 6
# 并行:
# 并行执行时间: 2
# [1, 4, 9, 16, 25, 36]

Process

采用Process必须注意的是,Process对象来创建进程,每一个进程占据一个CPU,所以要建立的进程必须 小于等于 CPU的个数。

如果启动进程数过多,特别是当遇到CPU密集型任务,会降低并行的效率。

#16.6.1.1. The Process class
from multiprocessing import Process, cpu_count
import os
import time
start_time = time.time()
def info(title):
#   print(title)
  if hasattr(os, 'getppid'): # only available on Unix
    print 'parent process:', os.getppid()
  print 'process id:', os.getpid()
  time.sleep(3)
def f(name):
  info('function f')
  print 'hello', name
if __name__ == '__main__':
#   info('main line')
  p_list = [] # 保存Process新建的进程
  cpu_num = cpu_count()
  for xx in xrange(cpu_num):
    p_list.append(Process(target=f, args=('xx_%s' % xx,)))
  for xx in p_list:
    xx.start()
  for xx in p_list:
    xx.join()
  print('spend time: %.2f' % (time.time() - start_time))
parent process: 11741
# parent process: 11741
# parent process: 11741
# process id: 12249
# process id: 12250
# parent process: 11741
# process id: 12251
# process id: 12252
# hello xx_1
# hello xx_0
# hello xx_2
# hello xx_3
# spend time: 3.04

进程间通信

Process和Pool均支持Queues 和 Pipes 两种类型的通信。

Queue 队列

队列遵循先进先出的原则,可以在各个进程间使用。

# 16.6.1.2. Exchanging objects between processes
# Queues
from multiprocessing import Process, Queue
def f(q):
  q.put([42, None, 'hello'])
if __name__ == '__main__':
  q = Queue()
  p = Process(target=f, args=(q,))
  p.start()
  print q.get()  # prints "[42, None, 'hello']"
  p.join()

pipe

from multiprocessing import Process, Pipe
def f(conn):
  conn.send([42, None, 'hello'])
  conn.close()
if __name__ == '__main__':
  parent_conn, child_conn = Pipe()
  p = Process(target=f, args=(child_conn,))
  p.start()
  print parent_conn.recv()  # prints "[42, None, 'hello']"
  p.join()

queue 与 pipe比较

Pipe() can only have two endpoints.

Queue() can have multiple producers and consumers.

When to use them

If you need more than two points to communicate, use a Queue().

If you need absolute performance, a Pipe() is much faster because Queue() is built on top of Pipe().

参考:

https://stackoverflow.com/questions/8463008/python-multiprocessing-pipe-vs-queue

共享资源

多进程应该避免共享资源。在多线程中,我们可以比较容易地共享资源,比如使用全局变量或者传递参数。

在多进程情况下,由于每个进程有自己独立的内存空间,以上方法并不合适。

此时我们可以通过共享内存和Manager的方法来共享资源。

但这样做提高了程序的复杂度,并因为同步的需要而降低了程序的效率。

共享内存

共享内存仅适用于 Process 类,不能用于进程池 Pool

# 16.6.1.4. Sharing state between processes
# Shared memory
from multiprocessing import Process, Value, Array
def f(n, a):
  n.value = 3.1415927
  for i in range(len(a)):
    a[i] = -a[i]
if __name__ == '__main__':
  num = Value('d', 0.0)
  arr = Array('i', range(10))
  p = Process(target=f, args=(num, arr))
  p.start()
  p.join()
  print num.value
  print arr[:]
# 3.1415927
# [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]

Manager Class

Manager Class 既可以用于Process 也可以用于进程池 Pool。

from multiprocessing import Manager, Process
def f(d, l, ii):
  d[ii] = ii
  l.append(ii)
if __name__ == '__main__':
  manager = Manager()
  d = manager.dict()
  l = manager.list(range(10))
  p_list = [] 
  for xx in range(4):
    p_list.append(Process(target=f, args=(d, l, xx)))
  for xx in p_list:
    xx.start()
  for xx in p_list:
    xx.join()
  print d
  print l
# {0: 0, 1: 1, 2: 2, 3: 3}
# [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3]

补充:python程序多进程运行时间计算/多进程写数据/多进程读数据

import time
time_start=time.time()
time_end=time.time()
print('time cost',time_end-time_start,'s')

单位为秒,也可以换算成其他单位输出

注意写测试的时候,函数名要以test开头,否则运行不了。

多线程中的问题:

1)多线程存数据:

def test_save_features_to_db(self):
    df1 = pd.read_csv('/home/sc/PycharmProjects/risk-model/xg_test/statis_data/shixin_company.csv')
    com_list = df1['company_name'].values.tolist()
    # com_list = com_list[400015:400019]
    # print 'test_save_features_to_db'
    # print(com_list)
    p_list = [] # 进程列表
    i = 1
    p_size = len(com_list)
    for company_name in com_list:
      # 创建进程
      p = Process(target=self.__save_data_iter_method, args=[company_name])
      # p.daemon = True
      p_list.append(p)
      # 间歇执行进程
      if i % 20 == 0 or i == p_size: # 20页处理一次, 最后一页处理剩余
        for p in p_list:
          p.start()
        for p in p_list:
          p.join() # 等待进程结束
        p_list = [] # 清空进程列表
      i += 1

总结:多进程写入的时候,不需要lock,也不需要返回值。

核心p = Process(target=self.__save_data_iter_method, args=[company_name]),其中target指向多进程的一次完整的迭代,arg则是该迭代的输入。

注意写法args=[company_name]才对,原来写成:args=company_name,args=(company_name)会报如下错:只需要1个参数,而给出了34个参数。

多进程外层循环则是由输入决定的,有多少个输入就为多少次循环,理解p.start和p.join;

def __save_data_iter_method(self, com):
    # time_start = time.time()
    # print(com)
    f_d_t = ShiXinFeaturesDealSvc()
    res = f_d_t.get_time_features(company_name=com)
    # 是否失信
    shixin_label = res.shixin_label
    key1 = res.shixin_time
    if key1:
      public_at = res.shixin_time
      company_name = res.time_map_features[key1].company_name
      # print(company_name)
      established_years = res.time_map_features[key1].established_years
      industry_dx_rate = res.time_map_features[key1].industry_dx_rate
      regcap_change_cnt = res.time_map_features[key1].regcap_change_cnt
      share_change_cnt = res.time_map_features[key1].share_change_cnt
      industry_dx_cnt = res.time_map_features[key1].industry_dx_cnt
      address_change_cnt = res.time_map_features[key1].address_change_cnt
      fr_change_cnt = res.time_map_features[key1].fr_change_cnt
      judgedoc_cnt = res.time_map_features[key1].judgedoc_cnt
      bidding_cnt = res.time_map_features[key1].bidding_cnt
      trade_mark_cnt = res.time_map_features[key1].trade_mark_cnt
      network_share_cancel_cnt = res.time_map_features[key1].network_share_cancel_cnt
      cancel_cnt = res.time_map_features[key1].cancel_cnt
      industry_all_cnt = res.time_map_features[key1].industry_all_cnt
      network_share_zhixing_cnt = res.time_map_features[key1].network_share_zhixing_cnt
      network_share_judge_doc_cnt = res.time_map_features[key1].network_share_judge_doc_cnt
      net_judgedoc_defendant_cnt = res.time_map_features[key1].net_judgedoc_defendant_cnt
      judge_doc_cnt = res.time_map_features[key1].judge_doc_cnt
      f_d_do = ShixinFeaturesDto(company_name=company_name, established_years=established_years,
                    industry_dx_rate=industry_dx_rate, regcap_change_cnt=regcap_change_cnt,
                    share_change_cnt=share_change_cnt, industry_all_cnt=industry_all_cnt,
                    industry_dx_cnt=industry_dx_cnt, address_change_cnt=address_change_cnt,
                    fr_change_cnt=fr_change_cnt, judgedoc_cnt=judgedoc_cnt,
                    bidding_cnt=bidding_cnt, trade_mark_cnt=trade_mark_cnt,
                    network_share_cancel_cnt=network_share_cancel_cnt, cancel_cnt=cancel_cnt,
                    network_share_zhixing_cnt=network_share_zhixing_cnt,
                    network_share_judge_doc_cnt=network_share_judge_doc_cnt,
                    net_judgedoc_defendant_cnt=net_judgedoc_defendant_cnt,
                    judge_doc_cnt=judge_doc_cnt, public_at=public_at, shixin_label=shixin_label)
      # time_end = time.time()
      # print('totally cost', time_end - time_start)
      self.cfdbsvc.save_or_update_features(f_d_do)
def save_or_update_features(self, shixin_features_dto):
    """
    添加或更新:
    插入一行数据, 如果不存在则插入,存在则更新
    """
    self._pg_util = PgUtil()
    p_id = None
    if isinstance(shixin_features_dto, ShixinFeaturesDto):
      p_id = str(uuid.uuid1())
      self._pg_util.execute_sql(
        self.s_b.insert_or_update_row(
          self.model.COMPANY_NAME,
          {
            self.model.ID: p_id,
            # 公司名
            self.model.COMPANY_NAME: shixin_features_dto.company_name,
            # 失信时间
            self.model.PUBLIC_AT: shixin_features_dto.public_at,
            self.model.SHIXIN_LABEL : shixin_features_dto.shixin_label,
            self.model.ESTABLISHED_YEARS: shixin_features_dto.established_years, 
            self.model.INDUSTRY_DX_RATE: shixin_features_dto.industry_dx_rate, 
            self.model.REGCAP_CHANGE_CNT: shixin_features_dto.regcap_change_cnt, 
            self.model.SHARE_CHANGE_CNT: shixin_features_dto.share_change_cnt, 
            self.model.INDUSTRY_ALL_CNT: shixin_features_dto.industry_all_cnt, 
            self.model.INDUSTRY_DX_CNT: shixin_features_dto.industry_dx_cnt, 
            self.model.ADDRESS_CHANGE_CNT: shixin_features_dto.address_change_cnt, 
            self.model.NETWORK_SHARE_CANCEL_CNT: shixin_features_dto.network_share_cancel_cnt,
            self.model.CANCEL_CNT: shixin_features_dto.cancel_cnt, 
            self.model.NETWORK_SHARE_ZHIXING_CNT: shixin_features_dto.network_share_zhixing_cnt,
            self.model.FR_CHANGE_CNT: shixin_features_dto.fr_change_cnt, 
            self.model.JUDGEDOC_CNT: shixin_features_dto.judgedoc_cnt, 
            self.model.NETWORK_SHARE_JUDGE_DOC_CNT: shixin_features_dto.network_share_judge_doc_cnt,
            self.model.BIDDING_CNT: shixin_features_dto.bidding_cnt, 
            self.model.TRADE_MARK_CNT: shixin_features_dto.trade_mark_cnt, 
            self.model.JUDGE_DOC_CNT: shixin_features_dto.judge_doc_cnt 
          },
          [self.model.ADDRESS_CHANGE_CNT,self.model.BIDDING_CNT,self.model.CANCEL_CNT,
           self.model.ESTABLISHED_YEARS,self.model.FR_CHANGE_CNT,self.model.INDUSTRY_ALL_CNT,
           self.model.INDUSTRY_DX_RATE,self.model.INDUSTRY_DX_CNT,self.model.JUDGE_DOC_CNT,
           self.model.JUDGEDOC_CNT,self.model.NETWORK_SHARE_CANCEL_CNT,self.model.NETWORK_SHARE_JUDGE_DOC_CNT,
           self.model.NETWORK_SHARE_ZHIXING_CNT,self.model.REGCAP_CHANGE_CNT,self.model.TRADE_MARK_CNT,
           self.model.SHARE_CHANGE_CNT,self.model.SHIXIN_LABEL,self.model.PUBLIC_AT]
        )
      )
    return p_id

函数中重新初始化了self._pg_util = PgUtil(),否则会报ssl error 和ssl decryption 的错误,背后原因有待研究!

**2)多进程取数据——(思考取数据为何要多进程)**
  def flush_process(self, lock): #需要传入lock;
    """
    运行待处理的方法队列
    :type lock Lock
    :return 返回一个dict
    """
    # process_pool = Pool(processes=20)
    # data_list = process_pool.map(one_process, self.__process_data_list)
    #
    # for (key, value) in data_list:
    #
    # 覆盖上期变量
    self.__dct_share = self.__manager.Value('tmp', {}) # 进程共享变量
    p_list = [] # 进程列表
    i = 1
    p_size = len(self.__process_data_list)
    for process_data in self.__process_data_list:  **#循环遍历需要同时查找的公司列表!!!self.__process_data_list包含多个process_data,每个process_data包含三种属性?类对象也可以循环????**
      # 创建进程
      p = Process(target=self.__one_process, args=(process_data, lock)) #参数需要lock
      # p.daemon = True
      p_list.append(p)
      # 间歇执行进程
      if i % 20 == 0 or i == p_size: # 20页处理一次, 最后一页处理剩余
        for p in p_list:
          p.start()
        for p in p_list:
          p.join() # 等待进程结束
        p_list = [] # 清空进程列表
      i += 1
    # end for
    self.__process_data_list = [] # 清空订阅
    return self.__dct_share.value
 def __one_process(self, process_data, lock):  #迭代函数
    """
    处理进程
    :param process_data: 方法和参数集等
    :param lock: 保护锁
    """
    fcn = process_data.fcn
    params = process_data.params
    data_key = process_data.data_key
    if isinstance(params, tuple):
      data = fcn(*params) #**注意:*params 与 params区别**
    else:
      data = fcn(params)
    with lock:
      temp_dct = dict(self.__dct_share.value)
      if data_key not in temp_dct:
        temp_dct[data_key] = []
      temp_dct[data_key].append(data)
      self.__dct_share.value = temp_dct

主程序调用:

def exe_process(self, company_name, open_from, time_nodes):
    """
    多进程执行pre订阅的数据
    :param company_name: 公司名
    :return:
    """
    mul_process_helper = MulProcessHelper()
    lock = Lock()
    self.__get_time_bidding_statistic(company_name, mul_process_helper)
    data = mul_process_helper.flush_process(lock)
    return data
 def __get_time_bidding_statistic(self, company_name, mul_process_helper):
    # 招投标信息
    process_data = ProcessData(f_e_t_svc.get_bidding_statistic_time_node_api, company_name,
                  self.__BIDDING_STATISTIC_TIME) **#此处怎么理解?ProcessData是一个类!!!**
    mul_process_helper.add_process_data_list(process_data)  #同时调用多个api???将api方法当做迭代????用于同时查找多个公司????
 def add_process_data_list(self, process_data):
    """
    添加用于进程处理的方法队列
    :type process_data ProcessData
    :param process_data:
    :return:
    """
    self.__process_data_list.append(process_data)
 class ProcessData(object):
  """
  用于进程处理的的数据
  """
  def __init__(self, fcn, params, data_key):
    self.fcn = fcn # 方法
    self.params = params # 参数
    self.data_key = data_key # 存储到进程共享变量中的名字

以上为个人经验,希望能给大家一个参考,也希望大家多多支持自学编程网。如有错误或未考虑完全的地方,望不吝赐教。

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